Get FEMA Public Assistance (PA) funding
get_public_assistance.RdProject- and county-level data on PA funding over time
Usage
get_public_assistance(
file_path = file.path(get_box_path(), "hazards", "fema", "public-assistance", "raw",
"PublicAssistanceFundedProjectsDetailsV2_2025_09_26.parquet"),
state_abbreviations = NULL
)Value
A dataframe of project-level funding requests and awards, along with variables that can be aggregated to the county level.
- id
A unique identifier for each project in the raw data. This field is not unique in the returned data due to crosswalking statewide projects to the county level. Refer to the details for additional information
- state_fips
A two-digit state identifier.
- state_name
The name of the state.
- state_abbreviation
The two-character USPS abbreviation for the state.
- county_fips
A five-digit county identifier.
- county_name
The name of the county.
- declaration_year
The year when the authorizing disaster declaration was made.
- disaster_number
The FEMA-created disaster number. This is unique at the disaster-state level; for example, Hurricane Helene has multiple disaster numbers associated with it, one per state that received an associated disaster declaration.
- incident_type
The type of disaster, e.g., "Hurricane".
- project_status
The current status of the funded PA project, e.g., "Active".
- damage_category_code
A letter code identifying the category of damages/what funds may be used for.
- damage_category_description
A descriptive characteristization of the damage category.
- pa_federal_funding_obligated
Obligated federal funding at the project
idlevel.- pa_federal_funding_obligated_split
Obligated federal attributed to the
id-by-county level. Refer to the details for additional information.
Details
These data have been crosswalked so that estimates can be aggregated at the county level. This is necessary (for county-level estimates) because many projects are statewide projects and do not have county-level observations in the data.
Analysts thus have two options for working with these data:
(1) De-select the variables suffixed with _split and then run distinct(df).
This will provide unique observations for projects; projects are both county-level
and statewide. These data can be aggregated to the state level but cannot be
comprehensively aggregated to the county level.
(2) Group the data at the county level and summarize to produce county-level
characterizations of PA projects and funding, using the _split-suffixed
variables to calculate funding totals. For example, this might look like:
The attribution of statewide projects to the county level occurs by proportionally attributing project costs based on county-level populations. For example, in a fictional state with two counties, one of population 10 and one of population 90, 10% of a statewide project's funding would be attributed to the first county and 90% of the project's funding to second county. Roughly 62 percent of the total PA funding returned by this function is for county-specific projects, and the remaining 38 percent is for statewide projects (as of 2025).